10 research outputs found
Learning Non-Uniform Hypergraph for Multi-Object Tracking
The majority of Multi-Object Tracking (MOT) algorithms based on the
tracking-by-detection scheme do not use higher order dependencies among objects
or tracklets, which makes them less effective in handling complex scenarios. In
this work, we present a new near-online MOT algorithm based on non-uniform
hypergraph, which can model different degrees of dependencies among tracklets
in a unified objective. The nodes in the hypergraph correspond to the tracklets
and the hyperedges with different degrees encode various kinds of dependencies
among them. Specifically, instead of setting the weights of hyperedges with
different degrees empirically, they are learned automatically using the
structural support vector machine algorithm (SSVM). Several experiments are
carried out on various challenging datasets (i.e., PETS09, ParkingLot sequence,
SubwayFace, and MOT16 benchmark), to demonstrate that our method achieves
favorable performance against the state-of-the-art MOT methods.Comment: 11 pages, 4 figures, accepted by AAAI 201
MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking
Standardized benchmarks have been crucial in pushing the performance of
computer vision algorithms, especially since the advent of deep learning.
Although leaderboards should not be over-claimed, they often provide the most
objective measure of performance and are therefore important guides for
research. We present MOTChallenge, a benchmark for single-camera Multiple
Object Tracking (MOT) launched in late 2014, to collect existing and new data,
and create a framework for the standardized evaluation of multiple object
tracking methods. The benchmark is focused on multiple people tracking, since
pedestrians are by far the most studied object in the tracking community, with
applications ranging from robot navigation to self-driving cars. This paper
collects the first three releases of the benchmark: (i) MOT15, along with
numerous state-of-the-art results that were submitted in the last years, (ii)
MOT16, which contains new challenging videos, and (iii) MOT17, that extends
MOT16 sequences with more precise labels and evaluates tracking performance on
three different object detectors. The second and third release not only offers
a significant increase in the number of labeled boxes but also provide labels
for multiple object classes beside pedestrians, as well as the level of
visibility for every single object of interest. We finally provide a
categorization of state-of-the-art trackers and a broad error analysis. This
will help newcomers understand the related work and research trends in the MOT
community, and hopefully shed some light on potential future research
directions.Comment: Accepted at IJC
Novel data association methods for online multiple human tracking
PhD ThesisVideo-based multiple human tracking has played a crucial role in many applications
such as intelligent video surveillance, human behavior analysis, and
health-care systems. The detection based tracking framework has become
the dominant paradigm in this research eld, and the major task is to accurately
perform the data association between detections across the frames.
However, online multiple human tracking, which merely relies on the detections
given up to the present time for the data association, becomes more
challenging with noisy detections, missed detections, and occlusions. To
address these challenging problems, there are three novel data association
methods for online multiple human tracking are presented in this thesis,
which are online group-structured dictionary learning, enhanced detection
reliability and multi-level cooperative fusion.
The rst proposed method aims to address the noisy detections and
occlusions. In this method, sequential Monte Carlo probability hypothesis
density (SMC-PHD) ltering is the core element for accomplishing the
tracking task, where the measurements are produced by the detection based
tracking framework. To enhance the measurement model, a novel adaptive
gating strategy is developed to aid the classi cation of measurements. In
addition, online group-structured dictionary learning with a maximum voting
method is proposed to estimate robustly the target birth intensity. It
enables the new-born targets in the tracking process to be accurately initialized
from noisy sensor measurements. To improve the adaptability of the
group-structured dictionary to target appearance changes, the simultaneous
codeword optimization (SimCO) algorithm is employed for the dictionary
update.
The second proposed method relates to accurate measurement selection
of detections, which is further to re ne the noisy detections prior to the tracking
pipeline. In order to achieve more reliable measurements in the Gaussian
mixture (GM)-PHD ltering process, a global-to-local enhanced con dence
rescoring strategy is proposed by exploiting the classi cation power of a mask
region-convolutional neural network (R-CNN). Then, an improved pruning
algorithm namely soft-aggregated non-maximal suppression (Soft-ANMS) is
devised to further enhance the selection step. In addition, to avoid the misuse
of ambiguous measurements in the tracking process, person re-identi cation
(ReID) features driven by convolutional neural networks (CNNs) are integrated
to model the target appearances.
The third proposed method focuses on addressing the issues of missed
detections and occlusions. This method integrates two human detectors
with di erent characteristics (full-body and body-parts) in the GM-PHD
lter, and investigates their complementary bene ts for tracking multiple
targets. For each detector domain, a novel discriminative correlation matching
(DCM) model for integration in the feature-level fusion is proposed, and
together with spatio-temporal information is used to reduce the ambiguous
identity associations in the GM-PHD lter. Moreover, a robust fusion
center is proposed within the decision-level fusion to mitigate the sensitivity
of missed detections in the fusion process, thereby improving the fusion
performance and tracking consistency.
The e ectiveness of these proposed methods are investigated using the
MOTChallenge benchmark, which is a framework for the standardized evaluation
of multiple object tracking methods. Detailed evaluations on challenging
video datasets, as well as comparisons with recent state-of-the-art
techniques, con rm the improved multiple human tracking performance